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All Journal International Journal of Advances in Applied Sciences ILKOM Jurnal Ilmiah
Rahman, Aedah Abd
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Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Birth data clustering to segmentation delays in birth certificate registration Hasmin, Erfan; Rahman, Aedah Abd
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp513-522

Abstract

Timely and accurate birth registration is essential for ensuring access to vital public services. This study focuses on clustering birth data to identify patterns in registration delays, using data mining techniques such as the K-means algorithm. By clustering birth data from Makassar City, Indonesia, based on various demographic and birth-related criteria, the study segments the data into groups that reflect both timely and delayed registrations. The optimal number of clusters is determined using the elbow and silhouette methods. Results show that a three-cluster configuration effectively captures patterns in birth registration delays, offering critical insights for policymakers. These findings provide a foundation for improving birth registration processes, ensuring more timely registration, and guiding data-driven public policy decisions.
A Hybrid BERT–RAG Model for Developing Knowledge-Validated Conversational Systems Anggreani, Desi; Ismawati, Ismawati; Auliyah, A. Inayah; Lukman, Lukman; Rahman, Aedah Abd; Nurmisba, Nurmisba; Akbar, Muh Ilham
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3126.30-42

Abstract

The transition of freshmen into the university environment requires adaptive and responsive information support. This study develops a chatbot system based on a hybrid BERT–RAG architecture integrated with the FAISS Index to provide automated consultation services for new students. The novelty of this research lies in the implementation of a faculty-based hierarchical knowledge structure and an adaptive multi-domain context mechanism—an approach not previously found in studies involving BERT–RAG for university onboarding services. This design enables the chatbot to deliver more relevant, personalized, and faculty-specific responses. The dataset was derived from three primary sources of information: the Faculty of Economics and Business (FEB), the Faculty of Teacher Training and Education (FKIP), and the Faculty of Engineering (FT), which were structured into a validated knowledge base in documents.json format. System evaluation was conducted across ten interaction scenarios using performance metrics including BERT Similarity, BLEU Score, ROUGE-1, ROUGE-2, and ROUGE-L. The system achieved excellent results, with average scores of 0.905 (BERT Similarity), 0.844 (BLEU), 0.876 (ROUGE-1), 0.820 (ROUGE-2), and 0.871 (ROUGE-L) and standard deviations below 0.1 across all metrics. Strong metric correlations (0.85–0.99) further indicate consistency between semantic understanding and generated text quality. Furthermore, the system effectively minimizes hallucination through validated knowledge integration and faculty-based reranking strategies. Overall, this research provides a significant contribution to the development of institutionally contextual educational chatbots capable of delivering accurate, natural, and responsive communication to support new student orientation in higher education
Co-Authors Akbar, Muh Ilham Andi Inayah Auliyah Anggreani, Desi Erfan Hasmin Ismawati Ismawati LUKMAN, LUKMAN Nurmisba, Nurmisba